Semester

Spring

Date of Graduation

2019

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Petroleum and Natural Gas Engineering

Committee Chair

ALI TAKBIRI BORUJENI

Committee Member

MING GU

Committee Member

SAMUEL AMERI

Committee Member

EBRAHIM FATHI

Abstract

In a geothermal field, power plants are designed for long-term electricity generation. Therefore, it is crucial to predict the future production and injection flow rates of the wells to determine the capacity of a power plant. In the designing of such power plants, calculations and estimations are based on the current future production, and injection flow rates and pressures in the geothermal field. Prediction of future production and injection flow rates also assist in building surface facilities with cost-efficient power plants. The most common problem in a power plant in geothermal fields is the inability to accurately estimate future expected production and planned injection flow rates. Due to this, power plants in the geothermal fields may not perform efficiently. The electricity generation cannot be continuous due to intermittent cycles of low and high energy generation from an inefficient geothermal power plant.

When it comes to power generation from geothermal reservoirs, the knowledge of the porous medium and heterogeneity quantification is vital but challenging. There are many reasons for inaccurate future forecasts, e.g., non-isothermal fluid flow, interference of condensable and non- condensable gases, high temperature and pressure zones, and imprecise reservoir borders, which add to the complexity of the problem. Mostly available and reliable measured data in the field are flow rates for producers and injectors, well-head pressure, wellhead temperature, valve position, off-set wells’ production, and injection data. In this thesis, Artificial Intelligence (AI) and machine learning (ML) technology, which is a relatively a new technology with high potentials for providing predictive solutions for the geothermal energy sector, is used to predict future production/ injection prediction using the reliable field data. AI might provide trustworthy resolutions for geothermal reservoirs modeling for forecasting since the model is based on the field measurements instead of making assumptions. AI is an alternative approach to conventional methods to eliminate dealing with uncertainties in the geothermal reservoirs.

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